def forward(self, graph_list, feature, m2v_feature, label_y, label_idx):
m2v_fc = self.input_drop(self.m2v_fc(m2v_feature))
feature = feature + m2v_fc
label_embed = self.label_embed(label_y)
label_embed = self.input_drop(label_embed)
feature_label = paddle.gather(feature, label_idx)
label_embed = paddle.concat([label_embed, feature_label], axis=1)
label_embed = self.label_mlp(label_embed)
feature = paddle.scatter(feature,
label_idx,
label_embed,
overwrite=True)
for idx, (sg, sub_index) in enumerate(graph_list):
temp_feat = []
skip_feat = paddle.gather(feature, sub_index, axis=0)
skip_feat = self.skips[idx](skip_feat)
skip_feat = self.norms[idx][0](skip_feat)
skip_feat = F.elu(skip_feat)
temp_feat.append(skip_feat)
for i in range(self.edge_type):
masked = sg.edge_feat['edge_type'] == i
m_sg = self.get_subgraph_by_masked(sg, masked)
if m_sg is not None:
feature_temp = self.gats[idx][i](m_sg, feature)
feature_temp = paddle.gather(feature_temp,
sub_index,
axis=0)
feature_temp = self.norms[idx][i + 1](feature_temp)
feature_temp = F.elu(feature_temp)
#skip_feat += feature_temp
temp_feat.append(feature_temp)
temp_feat = paddle.stack(temp_feat, axis=1) # b x 3 x dim
temp_feat_attn = self.path_attns[idx](temp_feat) # b x 3 x 1
temp_feat_attn = F.softmax(temp_feat_attn, axis=1)
temp_feat_attn = paddle.transpose(temp_feat_attn,
perm=[0, 2, 1]) # b x 1 x 3
skip_feat = paddle.bmm(temp_feat_attn, temp_feat)[:, 0]
skip_feat = self.path_norms[idx](skip_feat)
#feature = F.elu(skip_feat)
feature = self.dropout(skip_feat)
output = self.mlp(feature)
return output
def forward(self, x):
out_size = x.shape[2:]
for i in range(3):
out_size[i] = self.scale_factor[i] * out_size[i]
return F.elu(
self.bn(
self.conv3d(
F.interpolate(x,
size=out_size,
mode='trilinear',
align_corners=False,
data_format='NCDHW',
align_mode=0))))
def forward(self, graph_list, feature):
for idx, (sg, sub_index) in enumerate(graph_list):
#feature = paddle.gather(feature, sub_index, axis=0)
skip_feat = paddle.gather(feature, sub_index, axis=0)
skip_feat = self.skips[idx](skip_feat)
for i in range(self.edge_type):
masked = sg.edge_feat['edge_type'] == i
m_sg = self.get_subgraph_by_masked(sg, masked)
if m_sg is not None:
feature_temp = self.gats[idx][i](m_sg, feature)
feature_temp = paddle.gather(feature_temp,
sub_index,
axis=0)
skip_feat += feature_temp
feature = F.elu(self.norms[idx](skip_feat))
feature = self.dropout(feature)
output = self.mlp(feature)
return output
def forward(self, graphs, padded_feats):
out = self.aa_emb(graphs.node_feat["seq"])
if self.lm_model is not None:
pass # TODO: Sum output from lm_model using 'padded_feats' as input and variable 'out' above.
gcnn_concat = []
for gcnn in self.gcnn_list:
out = gcnn(graphs, out)
out = F.elu(out)
gcnn_concat.append(out)
out = paddle.concat(gcnn_concat, axis=1)
out = self.global_pool(graphs, out)
for fc in self.fc_list:
out = fc(out)
out = F.relu(out)
out = F.dropout(out, p=self.drop, training=self.training)
out = self.func_predictor(out)
return out
def forward(self, x):
return F.elu(self.bn(self.conv3d(x)))
def projection_3d(self, z):
z = F.elu(self.proj_3d_fc1(z))
return self.proj_3d_fc2(z)
